In a recent paper, Tinglong Dai, Risa Wolf, and Haiyang Yang wrote about unlearning in Medical AI.

With more and more CPU and storage thrown at Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) in general, the capacity to “memorise” information grows larger and larger with each generation of LLM. This is further accelerated by the capacity to add specific details to generalist LLMs using Retrieval Augmented Generation (RAG) and agents (e.g., with the ability to query real-world systems at the interface with the physical world).

LLMs are learning more, but what about unlearning? Dai and colleagues didn’t evoke the analogy with human memory: our capacity to learn more relies, in part, on our capacity to forget, to reorganise, to summarise, and to prioritise the learnt information. Sleep and stress play a role in this reorganisation of information; this was the overarching topic of my Ph.D. thesis [link]. I will de-prioritise the visual cues along the path leading to a bakery if I no longer go to this bakery (“unlearning”). However, practising navigation to the bakery improved this skill, and this improvement will serve me later when I need to go to another place (something I could call “secondary learning”). It may seem we diverge from AI, but Dai and colleagues actually start their paper with the EU GDPR possibility for a patient to remove their data from a database, wondering how this is technically possible with LLMs (where data is not structured like in a traditional relational database and where the way data is retrieved is often unknown).

The “unlearning” process in LLMs can be considered from three encapsulated levels: algorithm, legal, and ethical levels.

Continue reading “Notes en passant: how AI could unlearn in HEOR Modelling” →